文化規範に基づく説明可能な人間エージェント間の衝突解消
Culture-Based Explainable Human-Agent Deconfliction
人間の規則を人工エージェントの文化として写像する論証ベースのアーキテクチャを提案し、経路衝突解消のユーザー実験で説明が複雑な系ほど人間の性能を大きく向上させることを示した。
著者: Alex Raymond, Hatice Gunes, Amanda Prorok
分類: cs.MA, cs.HC, cs.LO, cs.RO
原文アブストラクト
Law codes and regulations help organise societies for centuries, and as AI systems gain more autonomy, we question how human-agent systems can operate as peers under the same norms, especially when resources are contended. We posit that agents must be accountable and explainable by referring to which rules justify their decisions. The need for explanations is associated with user acceptance and trust. This paper's contribution is twofold: i) we propose an argumentation-based human-agent architecture to map human regulations into a culture for artificial agents with explainable behaviour. Our architecture leans on the notion of argumentative dialogues and generates explanations from the history of such dialogues; and ii) we validate our architecture with a user study in the context of human-agent path deconfliction. Our results show that explanations provide a significantly higher improvement in human performance when systems are more complex. Consequently, we argue that the criteria defining the need of explanations should also consider the complexity of a system. Qualitative findings show that when rules are more complex, explanations significantly reduce the perception of challenge for humans.
